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####---- effective Relative Latent Model Complexity computation ----####
effective_rlmc<-function(df,
r.tau.prior,
MM=10^6,
output="sample",
step=ifelse(output=="prob", 0.03, NULL)){
# computation of the effective relative latent model complexity by MC sampling
# input:
# df: data frame containing a column df$sigma
# r.tau.prior: randomisation function for the prior
# MM: number of MC samples
# output: "sample", "summary", "prob"
# step: step value for bins for "prob"
# results:
# descriptive statistics or sample for the effective MRLMC
# sample: sample
# summary: descriptive statistics: summary: min, 0.25, 0.5, mean, 0.75, max
# prob: a data frame with x,y columns for plotting
set.seed(12567)
tau_sim<-r.tau.prior(MM)
kk<-length(df$sigma) # number od studies in the data frame
pdsum<-0
for (i in 1:kk){
sim_ICCi<-tau_sim^2/(tau_sim^2+df$sigma[i]^2)
pdsum<-pdsum+sim_ICCi
}
if(output=="sample"){
return(pdsum/kk)
}
if(output=="summary"){
return(summary(pdsum/kk))
}
if(output=="prob"){
data<-pdsum/kk
breaks <- seq(0,1,by=step)
bin <- cut(data, breaks, include.lowest = TRUE)
est <- tabulate(bin, length(levels(bin)))
y <- est/(diff(breaks)*length(data))
x<-breaks[-1]-step/2
return(data.frame(x=x, y=y))
}
}
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